Manual examination of tissue samples under a microscope is fraught with problems such as imprecision, inaccuracies, inter- and intra-pathologist discordance, lengthy examination times, and human inability to pick up subtle visual cues of differences in disease classes. Some communities in different parts of the globe have limited access to pathologists. On the other hand, advances in computer vision, machine learning algorithms, computer hardware technology, and computer networking technology, along with high throughput whole slide scanning technology will enable the emergence of computational pathology systems that can alleviate the aforementioned problems. With the emergence of deep learning and convolutional neural network algorithms and methods and the development of graphical processing unit (GPU) technologies, automated analysis of digitized magnified images of tissue samples will become possible.
Moreover, machine learning systems can now be trained to distinguish between not only the current disease class definitions prevalent in clinical pathology (such as benign vs. malignant, or various grades of cancer), but also between new disease class definitions that are more meaningful for treatment planning. For example, these new disease classes could be based on evidence of causes or effects of the disease such as genomic differences of diseased cells or outcome of a particular treatment or combination of treatments based on follow up data. These new disease classes may be too subtly different from each other for human pathologists to reliably recognize compared to an automated quantitative method. Use and automated recognition of such new class definitions in addition to existing disease classes used in a clinical setting may enable more personalized treatment planning for patients. This in turn will reduce side effects, discomfort, and treatment costs, and increase treatment effectiveness leading to the advancement of precision oncology.